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/*
* Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
* Copyright (C) 2017 The Android Open Source Project
* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include "Pad.h"
#include "Assert.h"
#include "Logging.h"
#include "internal/Dims.h"
#include <vector>
#include <cstring> // For 'memset'
bool padPrepare(const Shape& input, const int32_t* paddingsData, const Shape& paddingsShape,
Shape* output)
{
// Currently only 4D tensors are supported.
uint32_t numInputDims = getNumberOfDimensions(input);
ASSERT(numInputDims == 4);
// paddings need to be provided as a 2-D int32 tensor.
ASSERT(paddingsShape.type == OperandType::TENSOR_INT32);
ASSERT(getNumberOfDimensions(paddingsShape) == 2);
ASSERT(getSizeOfDimension(paddingsShape, 0) == numInputDims);
ASSERT(getSizeOfDimension(paddingsShape, 1) == 2);
std::vector<uint32_t> outDims(numInputDims);
for (uint32_t i = 0; i < numInputDims; ++i)
{
int32_t beforePadding = *paddingsData++;
int32_t afterPadding = *paddingsData++;
// Pad value has to be greater than equal to 0.
ASSERT(beforePadding >= 0 && afterPadding >= 0);
outDims[i] = beforePadding + getSizeOfDimension(input, i) + afterPadding;
}
output->type = input.type;
output->dimensions = outDims;
output->offset = input.offset;
output->scale = input.scale;
return true;
}
namespace
{
// From optimized_ops.h in TensorFlow Lite
template <typename T>
inline void Pad(const T* input_data, const Dims<4>& input_dims,
const std::vector<int>& left_paddings,
const std::vector<int>& right_paddings, T* output_data,
const Dims<4>& output_dims) {
const int output_batch = ArraySize(output_dims, 3);
const int output_height = ArraySize(output_dims, 2);
const int output_width = ArraySize(output_dims, 1);
const int output_depth = ArraySize(output_dims, 0);
const int left_b_padding = left_paddings[3];
const int left_h_padding = left_paddings[2];
const int left_w_padding = left_paddings[1];
const int left_d_padding = left_paddings[0];
const int right_b_padding = right_paddings[3];
const int right_h_padding = right_paddings[2];
const int right_w_padding = right_paddings[1];
const int right_d_padding = right_paddings[0];
const int input_depth = ArraySize(input_dims, 0);
if (left_b_padding != 0)
{
memset(output_data, 0, left_b_padding * output_height * output_width * output_depth *
sizeof(T));
}
for (int out_b = left_b_padding; out_b < output_batch - right_b_padding; ++out_b)
{
if (left_h_padding != 0)
{
memset(output_data + Offset(output_dims, 0, 0, 0, out_b), 0,
left_h_padding * output_width * output_depth * sizeof(T));
}
for (int out_h = left_h_padding; out_h < output_height - right_h_padding; ++out_h)
{
if (left_w_padding != 0)
{
memset(output_data + Offset(output_dims, 0, 0, out_h, out_b), 0,
left_w_padding * output_depth * sizeof(T));
}
for (int out_w = left_w_padding; out_w < output_width - right_w_padding; ++out_w)
{
if (left_d_padding != 0)
{
memset(output_data + Offset(output_dims, 0, out_w, out_h, out_b), 0,
left_d_padding * sizeof(T));
}
T* out = output_data +
Offset(output_dims, left_d_padding, out_w, out_h, out_b);
const T* in =
input_data + Offset(input_dims, 0, out_w - left_w_padding,
out_h - left_h_padding, out_b - left_b_padding);
memcpy(out, in, input_depth * sizeof(T));
if (right_d_padding != 0)
{
memset(
output_data + Offset(output_dims, output_depth - right_d_padding,
out_w, out_h, out_b),
0, right_d_padding * sizeof(T));
}
}
if (right_w_padding != 0)
{
memset(
output_data + Offset(output_dims, 0, output_width - right_w_padding,
out_h, out_b),
0, right_w_padding * output_depth * sizeof(T));
}
}
if (right_h_padding != 0)
{
memset(output_data + Offset(output_dims, 0, 0,
output_height - right_h_padding, out_b),
0, right_h_padding * output_width * output_depth * sizeof(T));
}
}
if (right_b_padding != 0)
{
memset(output_data +
Offset(output_dims, 0, 0, 0, output_batch - right_b_padding),
0,
right_b_padding * output_height * output_width * output_depth *
sizeof(T));
}
}
} // namespace
bool padGeneric(const uint8_t* inputData, const Shape& inputShape, const int32_t* paddings,
uint8_t* outputData, const Shape& outputShape)
{
int32_t numInputDims = static_cast<int32_t>(getNumberOfDimensions(inputShape));
std::vector<int> beforePadding;
std::vector<int> afterPadding;
// The lower level implementation expects the paddings in the reverse order.
for (int32_t i = numInputDims - 1; i >= 0; --i)
{
beforePadding.push_back(paddings[i * 2]);
afterPadding.push_back(paddings[i * 2 + 1]);
}
if (inputShape.type == OperandType::TENSOR_FLOAT32)
{
::Pad(reinterpret_cast<const float*>(inputData),
convertShapeToDims(inputShape),
beforePadding, afterPadding,
reinterpret_cast<float*>(outputData),
convertShapeToDims(outputShape));
}
else if (inputShape.type == OperandType::TENSOR_QUANT8_ASYMM)
{
::Pad(reinterpret_cast<const uint8_t*>(inputData),
convertShapeToDims(inputShape),
beforePadding, afterPadding,
reinterpret_cast<uint8_t*>(outputData),
convertShapeToDims(outputShape));
}
else
{
LOG(ERROR) << "Unsupported data type";
return false;
}
return true;
}
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